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In contrast, diffusion models are particularly effective at capturing fine\u2010grained features in images, making them well\u2010suited for these challenges. Therefore, RefineCatDiff, a refinement framework that leverages the strengths of diffusion models to achieve high\u2010quality medical image segmentation, is proposed. In this framework, an initial\u2010stage model predicts a coarse mask, which is subsequently refined by a diffusion model to generate a more accurate and detailed mask. Specifically, a categorical distribution\u2010based discrete diffusion model is developed for refinement, which better aligns with the characteristics of segmentation tasks, thereby enhancing its effectiveness. Moreover, the coarse mask is incorporated as prior knowledge into the diffusion process to further improve efficiency. 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