  Pixel and superpixel classifiers have become essential tools for EM
segmentation algorithms. Training these classifiers remains a major bottleneck
primarily due to the requirement of completely annotating the dataset which is
tedious, error-prone and costly. In this paper, we propose an interactive
learning scheme for the superpixel classifier for EM segmentation. Our
algorithm is "active semi-supervised" because it requests the labels of a small
number of examples from user and applies label propagation technique to
generate these queries. Using only a small set ($<20\%$) of all datapoints, the
proposed algorithm consistently generates a classifier almost as accurate as
that estimated from a complete groundtruth. We provide segmentation results on
multiple datasets to show the strength of these classifiers.
