  Weakly supervised medical image segmentation (MIS) using generative models is
crucial for clinical diagnosis. However, the accuracy of the segmentation
results is often limited by insufficient supervision and the complex nature of
medical imaging. Existing models also only provide a single outcome, which does
not allow for the measurement of uncertainty. In this paper, we introduce
DiffSeg, a segmentation model for skin lesions based on diffusion difference
which exploits diffusion model principles to ex-tract noise-based features from
images with diverse semantic information. By discerning difference between
these noise features, the model identifies diseased areas. Moreover, its
multi-output capability mimics doctors' annotation behavior, facilitating the
visualization of segmentation result consistency and ambiguity. Additionally,
it quantifies output uncertainty using Generalized Energy Distance (GED),
aiding interpretability and decision-making for physicians. Finally, the model
integrates outputs through the Dense Conditional Random Field (DenseCRF)
algorithm to refine the segmentation boundaries by considering inter-pixel
correlations, which improves the accuracy and optimizes the segmentation
results. We demonstrate the effectiveness of DiffSeg on the ISIC 2018 Challenge
dataset, outperforming state-of-the-art U-Net-based methods.
