  Deep learning requires large amounts of training data to be effective. For
the task of object segmentation, manually labeling data is very expensive, and
hence interactive methods are needed. Following recent approaches, we develop
an interactive object segmentation system which uses user input in the form of
clicks as the input to a convolutional network. While previous methods use
heuristic click sampling strategies to emulate user clicks during training, we
propose a new iterative training strategy. During training, we iteratively add
clicks based on the errors of the currently predicted segmentation. We show
that our iterative training strategy together with additional improvements to
the network architecture results in improved results over the state-of-the-art.
