The results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology workflows.
Abstract
Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning. We present a reproducible framework for evaluating uncertainty-aware segmentation under con- trolled clinical degradation. Our experiments use a synthetic multimodal brain tumor MRI cohort generated with a biophysical phantom simulator that follows the BraTS protocol. We train U-Net and Attention U-Net baselines for multi-class tumor sub-region segmentation and augment both models with Monte Carlo dropout to estimate per-voxel uncertainty. Across eight clinically motivated corruption types at five severity levels, we measure segmentation accuracy, calibration, failure detection, and selective prediction coverage. On clean data, Attention U-Net achieves a whole-tumor Dice of 0.990; under severe Gaussian noise, its performance falls to 0.089. Predictive uncertainty rises with degradation and tracks segmentation error (Pearson r = 0.53 under severity-3 Gaussian noise), allowing us to flag failures with an AUROC of 0.843. These results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology workflows. We release the code, trained models, and evaluation protocol to support direct reproduction.
A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.
Seyed Sina Ziaee, K. Ovens· Journal of Imaging· 0 citations
Medical image segmentation is crucial for clinical diagnosis and treatment planning. However, in complex scenarios, factors such as boundary ambiguity, annotation noise, and domain shift often lead models to be overconfident, limiting the interpretability and trustworthy deployability. To address this, we propose a "plug-and-play" evidence-guided framework for reliable medical image segmentation. By incorporating an evidential constraint to suppress "high-confidence but erroneous" predictions, and without altering the existing segmentation backbone architecture, we append a lightweight evidential prediction head to enable the model to simultaneously output segmentation results and pixel-wise uncertainty in a single forward pass. An error-driven uncertainty calibration term is further introduced to enhance the joint calibration of evidence strength and scale, improving the stability, interpretability, and consistency of the risk indicator with the true error. Moreover, to reduce the reliance on manual empirical thresholds when utilizing uncertainty, we design an adaptive threshold selection process based on a validation set. During deployment, this enables the output of a reference-free high-risk region prompt for quality control, and, when annotations are available, facilitates quantitative validation through geometric consistency metrics between the uncertain region and the actual error region. We systematically evaluate our framework on a representative backbone, U-Net, across multiple datasets, and further investigate its architectural applicability through cross-backbone experiments. The results demonstrate that, with negligible increase in inference cost, our method generally achieves a more consistent, spatially coherent uncertainty representation that better aligns with the true error, providing a practical tool for quality control and risk awareness in clinical deployment. The source code is available at: https://github.com/Xingchao003/EviRisk-Seg.
Chaojie Xing, Haolin Zhan, Rencheng Song et al.· Medical Image Analysis· 0 citations
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Musa Doğan, I. Ozkan· BMC Medical Imaging· 0 citations
This paper proposes a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures and applies a novel consistency constraint by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an edge-aware uncertainty loss.
Waqas Anwaar, Van Manh, Wufeng Xue et al.· Interdisciplinary Sciences C...· 0 citations
A Dynamic Uncertainty-aware Network (DynU-Net) is proposed, a multi-task framework that adaptively balances segmentation and classification through learnable per-task uncertainty parameters that consistently outperforms both single-task and existing multi-task baselines.
Ngoc Ly Tran, Thi Thu Thuy Nguyen, Ba-Hung Ngo et al.· Journal of Computational Des...· 0 citations
An Evidential Uncertainty-Guided Boundary (EUGB) loss is proposed to demonstrate that uncertainty information can indeed facilitate combating boundary segmentation errors, and empirical insights for selecting appropriate loss functions across different application scenarios are provided.
Na Zeng, Qiao Lin, Xingyue Wang et al.· IEEE Transactions on Medical...· 0 citations