Self-supervised physics-guided deep learning (PG-DL) approaches enable training neural networks without fully-sampled data. These methods split the available k-space measurements into two sets. One is used in the data consistency units of the unrolled network, while the other is used to define the loss. Although self-supervised learning performs well at moderately high acceleration rates, scarcity of acquired data at high acceleration rates degrades the reconstruction performance. In this work, we propose a multi-mask self-supervised learning approach, which retrospectively splits acquired measurement into multiple 2-tuples of disjoint sets. Proposed multi-mask self-supervised learning method outperforms its single-mask counterpart at high acceleration rates.