This paper addresses the challenge of sample-efficient ambiguous segmentation in uncertain environments such as wildfire spread, medical diagnosis, and autonomous driving. The authors evaluate several training-free sampling methods including particle guidance and SPELL, adapting them from natural image generation to discrete segmentation tasks. They also propose a novel clustering-based technique to encourage diverse predictions from diffusion models. Validation is performed on the LIDC medical dataset, a modified Cityscapes dataset for autonomous driving scenarios, and a new MMFire wildfire spread simulation dataset. The work demonstrates that training-free methods can effectively generate diverse plausible segmentation outcomes without additional training overhead.