In HRI scenarios, human factors like trust can greatly impact task performance and interaction quality. Recent research has confirmed that perceived robot proficiency is a major antecedent of trust. By making robots aware of their capabilities, we can allow them to choose when to perform low-confidence actions, thus actively controlling the risk of trust reduction. In this paper, we propose SCONE, a policy to learn self-confidence from experience using semantic action embeddings. Using an assistive cooking setting, we show that the semantic aspect allows SCONE to learn self-confidence faster than existing approaches, while also achieving promising performance in simple instructions following. Finally, we share results from a pilot study with 31 participants, showing that such a self-confidence-aware policy increases capability-based human trust.