  In this work, we study various hybrid models of entropy-based and
representativeness sampling techniques in the context of active learning in
medical segmentation, in particular examining the role of UMAP (Uniform
Manifold Approximation and Projection) as a technique for capturing
representativeness. Although UMAP has been shown viable as a general purpose
dimension reduction method in diverse areas, its role in deep learning-based
medical segmentation has yet been extensively explored. Using the cardiac and
prostate datasets in the Medical Segmentation Decathlon for validation, we
found that a novel hybrid combination of Entropy-UMAP sampling technique
achieved a statistically significant Dice score advantage over the random
baseline ($3.2 \%$ for cardiac, $4.5 \%$ for prostate), and attained the
highest Dice coefficient among the spectrum of 10 distinct active learning
methodologies we examined. This provides preliminary evidence that there is an
interesting synergy between entropy-based and UMAP methods when the former
precedes the latter in a hybrid model of active learning.
