  Uncertainty estimation in deep learning becomes more important recently. A
deep learning model can't be applied in real applications if we don't know
whether the model is certain about the decision or not. Some literature
proposes the Bayesian neural network which can estimate the uncertainty by
Monte Carlo Dropout (MC dropout). However, MC dropout needs to forward the
model $N$ times which results in $N$ times slower. For real-time applications
such as a self-driving car system, which needs to obtain the prediction and the
uncertainty as fast as possible, so that MC dropout becomes impractical. In
this work, we propose the region-based temporal aggregation (RTA) method which
leverages the temporal information in videos to simulate the sampling
procedure. Our RTA method with Tiramisu backbone is 10x faster than the MC
dropout with Tiramisu backbone ($N=5$). Furthermore, the uncertainty estimation
obtained by our RTA method is comparable to MC dropout's uncertainty estimation
on pixel-level and frame-level metrics.
