This is not a matter of terminology: they're related, but different concepts.
A consensus algorithm is one that allows all the participants in a distributed system to choose a value from a set in such a way that all the participants choose the same value. A solution to the consensus problem is a distributed algorithm that has the following properties:
- At the beginning, each participant $i$ has an initial value $v^0_i$.
- At the end, all the participants must have the same value: $\forall i, \forall j, v_i = v_j$.
- The common final value must be the initial value of at least one of the participants: $\exists i, v_i = v^0_i$.
A leader election algorithm is a special case of consensus algorithm where the set is the set of participants. The leader election problem consists of requiring a leader election whenever there is no leader. If there are failures, this may require running more than one election, because a new election becomes necessary when the current leader fails.
In the absence of failures, if you can solve consensus then you can solve the leader election problem: apply consensus with every participant initially choosing themselves. Conversely, if you can elect a leader, then you can solve consensus by having everybody choose the leader's value. This is often how consensus is solved in practice: choose a leader (using a specialized consensus algorithm), then the leader broadcasts its choice of value each time a consensus is needed.
However, if there are failures, this equivalence no longer holds. There are failure models where it's possible to solve consensus, but not to elect a leader. See Laura S. Sabely and Keith Marzullo, Election Vs. Consensus in Asynchronous Systems, Cornell University technical report, 1995. The gap is that certain failure detectors are sufficient to run a consensus algorithm, so they are sufficient to run a leader election, but they are not sufficient to solve the leader election problem, because they aren't sufficient to detect when a new leader election is needed.