  Active learning is a unique abstraction of machine learning techniques where
the model/algorithm could guide users for annotation of a set of data points
that would be beneficial to the model, unlike passive machine learning. The
primary advantage being that active learning frameworks select data points that
can accelerate the learning process of a model and can reduce the amount of
data needed to achieve full accuracy as compared to a model trained on a
randomly acquired data set. Multiple frameworks for active learning combined
with deep learning have been proposed, and the majority of them are dedicated
to classification tasks. Herein, we explore active learning for the task of
segmentation of medical imaging data sets. We investigate our proposed
framework using two datasets: 1.) MRI scans of the hippocampus, 2.) CT scans of
pancreas and tumors. This work presents a query-by-committee approach for
active learning where a joint optimizer is used for the committee. At the same
time, we propose three new strategies for active learning: 1.) increasing
frequency of uncertain data to bias the training data set; 2.) Using mutual
information among the input images as a regularizer for acquisition to ensure
diversity in the training dataset; 3.) adaptation of Dice log-likelihood for
Stein variational gradient descent (SVGD). The results indicate an improvement
in terms of data reduction by achieving full accuracy while only using 22.69 %
and 48.85 % of the available data for each dataset, respectively.
