  Cell tracking and segmentation assist biologists in extracting insights from
large-scale microscopy time-lapse data. Driven by local accuracy metrics,
current tracking approaches often suffer from a lack of long-term consistency.
To address this issue, we introduce an uncertainty estimation technique for
neural tracking-by-regression frameworks and incorporate it into our novel
extended Poisson multi-Bernoulli mixture tracker. Our uncertainty estimation
identifies uncertain associations within high-performing tracking-by-regression
methods using problem-specific test-time augmentations. Leveraging this
uncertainty, along with a novel mitosis-aware assignment problem formulation,
our tracker resolves false associations and mitosis detections stemming from
long-term conflicts. We evaluate our approach on nine competitive datasets and
demonstrate that it outperforms the current state-of-the-art on biologically
relevant metrics substantially, achieving improvements by a factor of
approximately $5.75$. Furthermore, we uncover new insights into the behavior of
tracking-by-regression uncertainty.
