Title: Revisiting Uncertainty Estimation for Node Classification: New Benchmark and Insights. Abstract: Uncertainty estimation is an important task that can be essential for high-risk applications of machine learning. This problem is especially challenging for node-level prediction in graph-structured data, as the samples (nodes) are interdependent. Recently, several studies addressed node-level uncertainty estimation. However, there is no established benchmark for evaluating these methods in a unified setup covering diverse distributional shift. In this paper, we address this problem and propose such a benchmark together with a technique for the controllable generation of data splits with various types of distributional shift. Importantly, besides the standard feature-based distributional shift, we also consider shifts specifically designed for graph-structured data. In summary, our benchmark consists of several graph datasets equipped with various distributional shift on which we evaluate the robustness of models and uncertainty estimation performance. This allows us to compare existing solutions in a unified setup. Moreover, we decompose the current state-of-the-art Dirichlet-based framework and perform an ablation study on its components. In our experiments, we demonstrate that when faced with complex yet realistic distributional shift, most models fail to maintain high classification performance and consistency of uncertainty estimates with prediction errors. However, ensembling techniques help to partially overcome significant drops in performance and achieve better results than distinct models. Among single-pass models, Natural Posterior Network with GNN encoder achieves the best performance.