Abstract This paper conducts a systematic study on the role of visual attention in the Unsupervised Video Object Segmentation (UVOS) task. By elaborately annotating three popular video segmentation datasets (DAVIS16, Youtube-Objects and SegTrackV 2) with dynamic eye-tracking data in the UVOS setting, for the fifirst time, we quantitatively verifified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgements during dynamic, task-driven viewing. Such novel observations provide an in-depth insight into the underlying rationale behind UVOS. Inspired by these fifindings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention Prediction (DVAP) in spatiotemporal domain, and Attention-Guided Object Segmentation (AGOS) in spatial domain. Our UVOS solution enjoys three major merits: 1) modular training without using expensive video segmentation annotations, instead, using more affordable dynamic fifixation data to train the initial video attention module and using existing fifixation-segmentation paired static/image data to train the subsequent segmentation module; 2) comprehensive foreground understanding through multi-source learning; and 3) additional interpretability from the biologically-inspired and assessable attention. Experiments on popular benchmarks show that, even without using expensive video object mask annotations, our model achieves compelling performance in comparison with stateof-the-arts.