This study investigates the usability of machine translation (MT) outputs into Slovak, a low-resource and morphologically rich language, by examining the alignment between human evaluations and automated, reference-less quality estimation (QE) models. It aims to address the gap in MT evaluation that typically focuses on adequacy and fluency rather than practical usability. A QE model incorporating fluency, adequacy, and complexity features was compared against human judgments. The statistical analysis revealed a moderate, significant correlation, which remained stable even after excluding an outlier evaluator. Inter-annotator agreement was generally low to moderate, highlighting the variability in human assessments. Nevertheless, multiple comparisons indicated no significant differences between the QE model and five of the seven human evaluators, suggesting the model aligns closely with expert judgment. These findings underscore the challenges of human MT evaluation, particularly subjective variability inherent in assessing translation usability, while demonstrating the potential of QE models to support scalable, high-quality MT assessment. This study is the first to explore both human consistency and QE alignment for Slovak, offering valuable insights for improving MT evaluation in low-resource, morphologically complex languages.