Cross-lingual named entity recognition (NER) aims to build an NER model that generalizes to the low-resource target language with labeled data from the high-resource source language. Current state-of-the-art methods typically combine self-training mechanism with contrastive learning paradigm, in order to develop discriminative entity clusters for cross-lingual adaptation. Despite the promise, we identify that these methods neglect two key problems: distribution skewness and pseudo-label bias, leading to indistinguishable entity clusters with small margins. To this end, we propose a novel framework, MARAL, which optimizes an adaptively reweighted contrastive loss to handle the class skewness and theoretically guarantees the optimal feature arrangement with maximum margin. To further mitigate the adverse effects of unreliable pseudo-labels, MARAL integrates a progressive cross-lingual adaptation strategy, which first selects reliable samples as anchors and then refines the remaining unreliable ones. Extensive experiments demonstrate that MARAL significantly outperforms the current state-of-the-art methods on multiple benchmarks, e.g., +2.04% on the challenging MultiCoNER dataset.