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Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to noninvasively study brain networks within individuals. Previous individual-specific network mappings do not account for intra-subject (within-subject) variability. Therefore, intrasubject variability might be mistaken for inter-subject (between-subject) differences. Here we propose a multi-session hierarchical Bayesian model (MS-HBM) that explicitly differentiates between intra-subject and inter-subject functional connectivity variability, as well as intersubject network spatial variability. Across three datasets, sensory-motor networks exhibited lower inter-subject, but higher intra-subject variability than association networks. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and taskfMRI data. MS-HBM parcellations from a single rs-fMRI session were comparable to a recent template-matching algorithm using five sessions. Individual-specific MS-HBM cortical parcellations were highly reproducible, while capturing inter-subject network differences. Inter-subject differences in the spatial arrangement of cortical networks could predict individuals’ cognition, personality and emotion, thus highlighting the importance of examining individual-specific network spatial configuration in rs-fMRI studies.