  High-quality training data play a key role in image segmentation tasks.
Usually, pixel-level annotations are expensive, laborious and time-consuming
for the large volume of training data. To reduce labelling cost and improve
segmentation quality, interactive segmentation methods have been proposed,
which provide the result with just a few clicks. However, their performance
does not meet the requirements of practical segmentation tasks in terms of
speed and accuracy. In this work, we propose EdgeFlow, a novel architecture
that fully utilizes interactive information of user clicks with edge-guided
flow. Our method achieves state-of-the-art performance without any
post-processing or iterative optimization scheme. Comprehensive experiments on
benchmarks also demonstrate the superiority of our method. In addition, with
the proposed method, we develop an efficient interactive segmentation tool for
practical data annotation tasks. The source code and tool is avaliable at
https://github.com/PaddlePaddle/PaddleSeg.
