SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

Medical image segmentation relies heavily on accurate uncertainty estimation to inform downstream clinical decisions. However, existing methods often require multiple inference passes or make restrictive assumptions about feature spaces. SegWithU addresses this limitation by introducing a single-forward-pass approach that leverages perturbation energy as a measure of uncertainty. This method enables efficient and reliable uncertainty estimation without requiring repeated inference or restrictive assumptions. By providing a more accurate assessment of uncertainty, SegWithU can improve the reliability of automated contours in medical image segmentation¹. This has significant implications for clinical decision support and downstream quantification. The development of SegWithU highlights the ongoing efforts to improve the accuracy and reliability of AI-based medical image analysis. As AI continues to play a larger role in medical imaging, the importance of robust uncertainty estimation will only continue to grow, making advancements like SegWithU crucial for ensuring the accuracy and safety of clinical decisions.