Uncertainty as risk: A plug-and-play evidence-guided framework with error-aligned calibration for medical image...

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.