  Collective insights from a group of experts have always proven to outperform
an individual's best diagnostic for clinical tasks. For the task of medical
image segmentation, existing research on AI-based alternatives focuses more on
developing models that can imitate the best individual rather than harnessing
the power of expert groups. In this paper, we introduce a single diffusion
model-based approach that produces multiple plausible outputs by learning a
distribution over group insights. Our proposed model generates a distribution
of segmentation masks by leveraging the inherent stochastic sampling process of
diffusion using only minimal additional learning. We demonstrate on three
different medical image modalities- CT, ultrasound, and MRI that our model is
capable of producing several possible variants while capturing the frequencies
of their occurrences. Comprehensive results show that our proposed approach
outperforms existing state-of-the-art ambiguous segmentation networks in terms
of accuracy while preserving naturally occurring variation. We also propose a
new metric to evaluate the diversity as well as the accuracy of segmentation
predictions that aligns with the interest of clinical practice of collective
insights.
