  Biomedical image segmentation is a crucial part of both scientific research
and clinical care. With enough labelled data, deep learning models can be
trained to accurately automate specific biomedical image segmentation tasks.
However, manually segmenting images to create training data is highly labor
intensive and requires domain expertise. We present \emph{ScribblePrompt}, a
flexible neural network based interactive segmentation tool for biomedical
imaging that enables human annotators to segment previously unseen structures
using scribbles, clicks, and bounding boxes. Through rigorous quantitative
experiments, we demonstrate that given comparable amounts of interaction,
ScribblePrompt produces more accurate segmentations than previous methods on
datasets unseen during training. In a user study with domain experts,
ScribblePrompt reduced annotation time by 28% while improving Dice by 15%
compared to the next best method. ScribblePrompt's success rests on a set of
careful design decisions. These include a training strategy that incorporates
both a highly diverse set of images and tasks, novel algorithms for simulated
user interactions and labels, and a network that enables fast inference. We
showcase ScribblePrompt in an interactive demo, provide code, and release a
dataset of scribble annotations at https://scribbleprompt.csail.mit.edu
