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In this paper, we explore the possibility to increase the training examples without laborious data collection and annotation for long-tailed instance segmentation. We find that an abundance of instance segments can potentially be obtained freely from object-centric images, according to two insights: (i) an object-centric image usually contains one salient object in a simple background; (ii) objects from the same class often share similar appearances or similar contrasts to the background. Motivated by these insights, we propose a simple and scalable framework FREESEG for extracting and leveraging these “free” object segments to facilitate model training. Concretely, we investigate the similarity among object-centric images of the same class to propose candidate segments of foreground instances, followed by a novel ranking of segment quality. The resulting high quality object segments can then be used to augment the existing long-tailed datasets, e.g., by copying and pasting the segments onto the original training images. Extensive experiments show that FREESEG yields substantial improvements on top of strong baselines and achieves state-of-the-art accuracy for segmenting rare object categories.