  Intra-operative anticipation of instrument usage is a necessary component for
context-aware assistance in surgery, e.g. for instrument preparation or
semi-automation of robotic tasks. However, the sparsity of instrument
occurrences in long videos poses a challenge. Current approaches are limited as
they assume knowledge on the timing of future actions or require dense temporal
segmentations during training and inference. We propose a novel learning task
for anticipation of instrument usage in laparoscopic videos that overcomes
these limitations. During training, only sparse instrument annotations are
required and inference is done solely on image data. We train a probabilistic
model to address the uncertainty associated with future events. Our approach
outperforms several baselines and is competitive to a variant using richer
annotations. We demonstrate the model's ability to quantify task-relevant
uncertainties. To the best of our knowledge, we are the first to propose a
method for anticipating instruments in surgery.
