  Diffusion models have shown impressive performance for generative modelling
of images. In this paper, we present a novel semantic segmentation method based
on diffusion models. By modifying the training and sampling scheme, we show
that diffusion models can perform lesion segmentation of medical images. To
generate an image specific segmentation, we train the model on the ground truth
segmentation, and use the image as a prior during training and in every step
during the sampling process. With the given stochastic sampling process, we can
generate a distribution of segmentation masks. This property allows us to
compute pixel-wise uncertainty maps of the segmentation, and allows an implicit
ensemble of segmentations that increases the segmentation performance. We
evaluate our method on the BRATS2020 dataset for brain tumor segmentation.
Compared to state-of-the-art segmentation models, our approach yields good
segmentation results and, additionally, detailed uncertainty maps.
