Researchers have developed a new method for medical image segmentation called SegWithU. It's designed to estimate the uncertainty of automated contours in medical images, which is crucial for downstream quantification and clinical decision support. Unlike other methods that require repeated inference or restrictive assumptions, SegWithU uses a lightweight framework that can be applied to a frozen pretrained segmentation backbone. This allows it to produce two voxel-wise uncertainty maps: one for probability tempering and another for error detection and selective prediction. In experiments on three medical image datasets, SegWithU outperformed other single-forward-pass methods in terms of accuracy and consistency. --- Why it matters: This matters because accurate medical image segmentation is critical for clinical decision support, and reliable uncertainty estimation can help prevent errors. By providing a practical and effective method for uncertainty modeling, SegWithU has the potential to improve the reliability of automated contouring in medical imaging. Source: https://arxiv.org/abs/2604.15271