Abstract: Due to rapid progress in optical imaging technologies, contemporary neural calcium imaging studies can monitor the dynamics of 10,000 or more neurons at once in the brains of awake behaving mammals. After automated extraction of the neurons' putative locations, a typical experiment involves extensive human labor to cull false-positive cells from the data, a process called \emph{cell sorting.} Efforts to automate cell sorting via the use of trained models either employ pre-trained, suboptimal classifiers or require reduced but still substantial human labor to train dataset-specific classifiers. In this workshop paper, we introduce an active-learning accelerated cell-sorting paradigm, termed ActSort, which establishes an online feedback loop between the human annotator and the cell classifier. To test this paradigm, we designed a first-of-a-kind benchmark by curating large-scale calcium imaging datasets from 5 mice, with approximately 40,000 cell candidates in total. Each movie was annotated by 4 (out of 6 total) human annotators, yielding about 160,000 total annotations. With this approach, we tested two active learning strategies, discriminative active learning (DAL) and confidence-based active learning (CAL). To create a baseline representing the traditional strategy, we performed random and first-to-last annotations, in which cells are annotated in either a random order or the order they are received from the cell-extraction algorithm. Our analysis revealed that, even when using the active learning-derived results of $<5$% of the human-annotated cells, CAL surpassed human performance levels in both precision and recall. In comparison, the first-to-last strategy required $80$\% of the cells to be annotated to achieve the same mark. By decreasing the human labor needed from hours to minutes while also enabling more accurate predictions than a typical human annotator, ActSort overcomes a bottleneck in neuroscience research and enables rapid pre-processing of large-scale brain-imaging datasets.