MedSegDiff applies diffusion probabilistic models to medical image segmentation tasks. It uses denoising diffusion processes where a neural network learns to reverse a gradual noising process to produce segmentation masks. The framework includes DPM-Solver integration for accelerated sampling, reducing inference steps from 1000 to 20 while maintaining accuracy. It supports both 2D and 3D medical imaging datasets including BRATS for brain tumor segmentation.