SegWithU：单次前向的医学分割不确定性估计 SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

精读摘要

可靠的医学图像分割不确定性估计至关重要——自动勾画的轮廓直接进入下游量化与临床决策支持。强不确定性方法大多需要重复推理，而高效的单次前向替代方案要么失败排序能力弱，要么依赖严格的特征空间假设。SegWithU 是一个事后框架，给冻结的预训练分割骨干加一个轻量不确定性头，利用骨干中间特征，把不确定性当作「扰动能量」来建模，兼顾效率与风险感知。 Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support. Strong methods require repeated inference, while efficient single-forward-pass alternatives often rank failures poorly or rely on restrictive assumptions. SegWithU augments a frozen pretrained backbone with a lightweight uncertainty head, modeling uncertainty as perturbation energy from intermediate features.