Deep unfolding networks (DUN) have emerged as a reliable iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction.However, conventional DUN aims to reconstruct all the missing information within the entire null space in each iteration. Thus the reconstruction quality could be degraded due to the cumulative errors.In this work, we propose a Progressive Divide-And-Conquer (PDAC) strategy, aiming to break down the subsampling process in the actual severe degradation and thus perform reconstruction sequentially.Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI, we present a rigorous derivation of the proposed PDAC framework, which could be further unfolded into an end-to-end trainable network.Specifically, each iterative stage in PDAC focuses on recovering a distinct moderate degradation according to the decomposition.Furthermore, as part of the PDAC iteration, such decomposition is adaptively learned as an auxiliary task through a degradation predictor which provides an estimation of the decomposed sampling mask.Following this prediction, the sampling mask is further integrated via a severity conditioning module to ensure awareness of the degradation severity at each stage.Extensive experiments demonstrate that our proposed method achieves superior performance on the publicly available fastMRI and Stanford2D FSE datasets in both single-coil and multi-coil settings.