We present a comprehensive and pedagogical formulation of Bayesian multiparameter quantum estimation, providing explicit conditions for achieving minimum quadratic losses. Within this framework, we analyse the role of measurement incompatibility and establish its quantitative effect on attainable precision. We achieve this by deriving upper bounds based on the pretty good measurement—a notion originally developed for hypothesis testing—combined with the evaluation of the Nagaoka–Hayashi lower bound. In general, we prove that, as in the many-copy regime of local estimation theory, incompatibility can at most double the minimum loss relative to the idealised scenario in which individually optimal measurements are assumed jointly implementable. This result implies that, in many practical situations, the latter may provide a sufficient and computationally efficient benchmark without solving the full optimisation problem. Our results, which we illustrate through a range of applications, including discrete quantum phase imaging, phase and dephasing estimation, and qubit sensing, provide analytical and numerical tools for assessing ultimate precision limits and the role of measurement incompatibility in Bayesian multiparameter quantum metrology. We also provide an open-source package that implements all bounds discussed here, enabling practical evaluation and comparison across quantum metrological models.