The emergence of heterogeneity in high-performance computing, which harnesses under one integrated system several platforms of different architectures, also led to the development of innovative cross-platform programming models. Along with the expectation that these models will yield computationally intensive performance, there is demand for them to provide a reasonable degree of performance portability. Therefore, new tools and metrics are being developed to measure and calculate the level of performance portability of applications and programming models. The ultimate measure of performance portability is performance efficiency. Performance efficiency refers to the achieved performance as a fraction of some peak theoretical or practical baseline performance. Application efficiency approaches are the most popular and attractive performance efficiency measures among researchers because they are simple to measure and calculate. Unfortunately, the way they are used yields results that do not make sense, while violating one of the basic criteria that defines and characterizes the performance portability metrics. In this paper, we demonstrate how researchers currently use application efficiency to calculate the performance portability of applications and explain why this method deviates from its original definition. Then, we show why the obtained results do not make sense and propose practical solutions that satisfy the definition and criteria of performance portability metrics. Finally, we present a new performance efficiency approach called portability efficiency, which is immune to the shortcomings of application efficiency and substantially improves the aspect of portability when calculating performance portability.