Soft clustering refers to clustering analysis methods that not only assign instances to clusters, but also provide indication about the uncertainty in cluster assignments. In this article we focus on the interplay between soft clustering and explainable AI, by adopting a user-oriented perspective aimed at comparing different (soft) clustering approaches in terms of their differing effectiveness in conveying uncertainty to users. To this aim, we designed a simulated, but realistic, medical decision-making problem in which users had to take a clinically relevant decision with the support of a clustering algorithm, and analyzed differences in the ability of different methods to convey uncertainty as well as in how users perceived their usefulness and clarity. Our results and statistical analysis providing initial, but suggestive, empirical evidence towards the differing capabilities of soft clustering approaches to convey uncertainty.