Uncertainty as risk: A plug-and-play evidence-guided framework with error-aligned calibration for medical image segmentation.
The results demonstrate that the proposed "plug-and-play" evidence-guided framework for reliable medical image segmentation 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.