Pose estimation systems are used in a variety of fields, from sports analytics to livestock care. Given their potential impact, it is paramount to systematically test their behaviour and potential for failure. This is a complex task due to the oracle problem and the high cost of manual labelling necessary to build ground truth keypoints. This problem is, in turn, exacerbated by the fact that different applications will require systems to focus on different subjects (e.g., human versus animal) or landmarks (e.g., only extremities versus whole body and face), which makes labelled test data rarely reusable. To combat these problems we propose MeT-Pose, a metamorphic testing framework for pose estimation systems that bypasses the need for manual annotation while assessing the performance of these systems under different circumstances. MeT-Pose thus allows users of pose estimation systems to assess the systems in conditions that more closely relate to their application without having to label an ad-hoc test dataset or to rely only on available datasets, which may not be adapted to their application domain. While we define MeT-Pose in general terms, we also present a non-exhaustive list of metamorphic rules that represent common challenges in computer vision applications, as well as a specific way to evaluate these rules. We then experimentally show the effectiveness of MeT-Pose by applying it to Mediapipe Holistic, a state of the art human pose estimation system, with the FLIC and PHOENIX datasets. With these experiments, we outline numerous ways in which the outputs of MeT-Pose can uncover faults in pose estimation systems at a similar or higher rate than classic testing using hand labelled data, and show that users can tailor the rule set they use to the faults and level of accuracy relevant to their application.