Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching

arXiv:2507.22418v3 Announce Type: replace-cross Abstract: Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression ability of generative models. While current diffusion-based approaches have demonstrated impressive performance in approximating the data distrib